F
Francesco Amato
Researcher at University of Naples Federico II
Publications - 299
Citations - 7180
Francesco Amato is an academic researcher from University of Naples Federico II. The author has contributed to research in topics: Linear system & Nonlinear system. The author has an hindex of 35, co-authored 266 publications receiving 6150 citations. Previous affiliations of Francesco Amato include Magna Græcia University & Mediterranea University of Reggio Calabria.
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New conditions for finite‐time stability of impulsive dynamical systems via piecewise quadratic functions
TL;DR: In this article , the use of time-varying piecewise quadratic functions is investigated to characterize the finite-time stability of state-dependent impulsive dynamical linear systems.
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Machine Learning Approaches with Textural Features to Calculate Breast Density on Mammography
Mario Sansone,Roberta Fusco,Francesca Grassi,Gianluca Gatta,Maria Paola Belfiore,Francesca Angelone,Carlo Ricciardi,Alfonso Maria Ponsiglione,Francesco Amato,Roberta Galdiero,Roberto Grassi,Vincenza Granata +11 more
TL;DR: In this paper , textural features were extracted only from breast parenchyma with which to train classifiers, thanks to the aid of ML algorithms, and the best accuracy was not influenced by the choice of the features selection approach.
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Roban: A Parameter Robustness Analysis Tool and its Flight Control Applications
Francesco Amato,L. Verde +1 more
TL;DR: In this paper, the authors present some applications of the software ROBAN, developed at the Centro Italiano Ricerche Aerospaziali (CIRA), developed to analyse stability and performances of linear systems subject to uncertain parameters, and has been shown to be particularly useful to deal with the robustness problems which arise in the flight control field.
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Reverse Engineering Partially-Known Interaction Networks from Noisy Data
Francesco Montefusco,Francesco Montefusco,Carlo Cosentino,Jongrae Kim,Francesco Amato,Declan G. Bates +5 more
TL;DR: A new inference algorithm, PACTLS, is introduced, which combines methods to exploit mechanisms underpinning scale–free networks generation, i.e. network growth and preferential attachment, with a technique to optimally reduce the effects of measurement noise in the data on the reliability of the inference results.